dfm

(function () {

    var root = this;

    root.Slider = function (target, min, max, on_finish, on_change) {
        var _this = this;
        var w = 260, h = 20, margin = 10, radius = 5;

        // On change event.
        this.on_change = function () {};

dfm

cd ~

git clone https://github.com/davidwhogg/TheThresher.git
cd TheThresher

virtualenv venv --distribute
. venv/bin/activate

pip install numpy
pip install -r requirements.txt

dfm

#!/usr/bin/env python

from matplotlib import rc
rc("font", family="serif", size=12)
rc("text", usetex=True)

import daft

pgm = daft.PGM([3.5, 3], origin=[0, -0.5])

dfm

Copyright (c) 2012–2013 Daniel Foreman-Mackey

Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

dfm

Usage

import sn

samples = np.array([...])  # Some numpy array of samples.

pars = sn.fit_skew_normal(samples)

dfm

import cPickle as pickle

for pos, lnprob, rstate in sampler.sample(pos0, iterations=500, storechain=False):
    # First save the positions as you usually would. Following: http://dfm.io/RrU8w8

    # Save the current state.
    with open("state.pkl", "w") as f:
        pickle.dump([pos, lnprob, rstate], f, -1)

#

dfm

First install the Python module:

pip install casjobs

Then set the environment variables:

export CAS_DR9_WSID="1234567890" # get yours here: http://skyserver.sdss3.org/CasJobs/ChangeDetails.aspx

dfm

fs = []
for i in range(10):
    fs.append(lambda: i)
print [f() for f in fs]

dfm

import numpy as np
import emcee


def lnprob(x, ivar):
    return -0.5 * np.array([np.sum(ivar * x ** 2), ])

ndim, nwalkers = 10, 100
ivar = 1. / np.random.rand(ndim)
p0 = [np.random.rand(ndim) for i in range(nwalkers)]

dfm

The script works.py runs fine and give reasonable results but the script notworking.py throws a cPickle.UnpicklingError and then stalls. The only difference is the line:

nwalkers = 100  # Works.
nwalkers = 150  # Doesn't work.